There are many existing and potential uses for automated facial expression recognition. Perhaps the best known use is the smile detection in digital cameras. There are others, including facial detection in response to various external stimuli, such as consumer reactions to advertisements, product displays, labeling, packaging, and pricing; voter reactions to talking points and evaluation of debate performances; and still other uses.
A need exists for improved methods and apparatus for automatically detecting and classifying facial expressions, including expressions responsive to external stimuli. One approach for developing such systems relies on machine learning techniques. Such techniques typically use large numbers of example images (which term herein indicates still images as well as videos) to discover the features that discriminate between different expression categories. A need also exists for efficient methods to generate and collect data that can be used for training automatic expression classifiers. The design of these data collection methods may be quite important for the success of the machine-learning expression classifiers that machine-learn using the collected data.